Calibration of electrical parameters in a deep learning artificial neural network
AssigneeSilicon Storage Technology, Inc.
InventorHieu Van Tran
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Abstract
Numerous examples are disclosed for performing calibration of various electrical parameters in a deep learning artificial neural network. In one example, a system comprises a digital-to-analog converter for receiving an input of k bits and generating a first analog output, a mapping scalar for converting the first analog output into a second analog output, and an analog-to-digital converter for generating an output of n bits from the second analog output, where n is a different value than k.
Claims27
A system comprising:
- a digital-to-analog converter for receiving an input of k bits and generating a first analog output;
- a mapping scalar for converting the first analog output into a second analog output;
- and
- an analog-to-digital converter for generating an output of n bits from the second analog output, where n is a different value than k.
A system comprising:
- a reference array;
- a bias generator for generating one or more bias voltages based on the reference array;
- and
- a digital-to-analog converter for generating analog signals to apply to a non-volatile memory array in response to digital inputs and the generated one or more bias voltages.
A system comprising:
- a digital-to-analog converter to generate voltages for linear or sub-threshold operation of non-volatile memory cells in an array;
- and
- a mapping block for translating generated voltages from the digital-to-analog converter into an analog output.
A system comprising:
- a capacitor bank comprising a plurality of capacitors, each of the plurality of capacitors storing a different voltage;
- a multiplexor for outputting a voltage of one of the plurality of capacitors in response to a digital input;
- and
- a buffer for receiving the output voltage from the multiplexor and generating a bias voltage.
A system comprising:
- an operational amplifier comprising an inverting input, a non-inverting input, and an output;
- an offset capacitor comprising a first terminal and a second terminal, the second terminal connected to the inverting input;
- a first switch;
- a second switch;
- and
- a third switch;
- wherein in a first mode, the first switch is opened, the second switch is closed to connect the output of the operational amplifier to the inverting input, and the third switch is closed to connect the first terminal of the offset capacitor to the non-inverting input;
- wherein in a second mode, the first switch is closed to connect the output of the operational amplifier to the first terminal of the capacitor, the second switch is opened, and the third switch is opened.
A system comprising:
- a first global digital-to-analog generator;
- and
- a second global digital-to-analog generator.
A system comprising:
- a first array of non-volatile memory cells arranged in rows and columns;
- a plurality of bitlines, wherein each bitline is coupled to a respective column in the first array;
- and
- a sampled neuron circuit to receive current from the plurality of bitlines and to generate an analog output from a sampling of the current from the plurality of bitlines, the analog output comprising a neuron current in a neural network.
Dependencies are inferred from the claim text.